Integration of C band SAR and optical temporal data for identification of paddy fields
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Integration of C band SAR and optical temporal data for identification of paddy fields Mangesh M. Deshpande1 · Anil Kumar2 · T. P. Singh1 Received: 20 January 2020 / Accepted: 16 April 2020 © Springer Nature Switzerland AG 2020
Abstract The problem of identification of single crop fields is a challenge when single date optical remote sensing image is used. The use of temporal images solves this problem. However, issues like cloud cover in optical images influence accuracy of results. Microwave data, which penetrate through the atmosphere, solve this problem. The existence of mixed pixels in satellite images and nonlinearity in image classification is also overlooked. These issues were considered and worked on by integrating C band RISAT-1 with Formosat-2 temporal images and using possibilistic c-means classifier with similarity and dissimilarity norms to identify late transplanted paddy (Oryza sativa) fields in Haridwar District of India. Three datasets in different temporal combinations of microwave and optical images were classified for various similarity and dissimilarity norms for different values of weighted constant. Favorable results were achieved for Manhattan and mean absolute difference norm at weighted constant m = 1.3. Classification of late transplanted paddy for datasets containing multiple RISAT-1 and single Formosat-2 images with transplanting, growth stages was found to yield best results as compared to other combination of temporal images. Keywords Soft classification · RISAT-1 · Formosat-2 · Possibilistic c-means · Weighted constant
1 Introduction Identification of a single crop has an advantage for the government as they can undertake different policies for the masses as well as regulate import and export strategy. Crop type maps often come to rescue of national and regional agricultural. They provide information to facilitate water resource planning for irrigation [1, 2], crop yield assessment and forecasting [3, 4] as well as mapping soil productivity [5]. A lot of fluctuations have been seen in the grains market especially in the last decade. The production of wheat and rice dropped from 63 to 16 MT between 2002 and 2007 [6]. This situation was of major concern for the national food security. Hence, for efficient analysis of such
conditions, monitoring single crop for its yield, acreage and agricultural pattern is essential [7]. Orthodox methods to compose crop type maps are based on ground surveying and census and record keeping [8]. These methods lack standardization. In order to standardize, the continuous nature of collecting information using remotely sensing satellite has proven efficient [9–11]. Satellite images obtained from different sensors and of different time durations can be clubbed together to obtain datasets with relatively low spectral dimensions. Many studies have exploited the use of optical images for carrying out crop-based analysis [12–15]. One major concern related to the optical data still remains the validity of how accurately the atmospheric corrections are don
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